Intelligent mining algorithm for complex medical data based on deep learning

Li, Xiaofeng; Dong, Li; Deng, Yuanbei; Xing, Jinming

doi:10.1007/s12652-020-02239-w

Cited by 12 publications

(2 citation statements)

References 21 publications

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“…DL allows description, prediction, dimensionality reduction on complex data. Li et al [41] were interested in extracting ARs using DL to speed up the extraction time on complex medical data that has been transformed to be analyzed using DL. In particular, the CNN architecture (for application examples, see [34,58], etc.)…”

Section: Arm and Deeplearningmentioning

confidence: 99%

Association Rules Mining

Hamdad

Benatchba

2021

SN COMPUT. SCI.

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Association rules mining (ARM) is an unsupervised learning task. It is used to generate significant and relevant association rules among items in a database. APRIORI and FP-GROWTH are the most popular and used algorithms nowadays for extracting such rules. They are exact methods that consist of two phases. First, frequent itemsets are generated. Then, the latter are used to generate rules. The main drawback of both algorithms is their high execution time. To overcome this drawback, metaheuristics have been proposed. Moreover, to optimize the execution time, since the amount of data is in continuous growth, some parallel architectures can be found in the literature. In this paper, we present an overview of existing literature that investigate the ARM problem using both metaheuristics and parallelism. We will focus on the recent algorithms that tackle these problems using approximate methods and GPU. We will present a non-exhaustive classification of different algorithms according to the type of execution (sequential or parallel) and type of method (exact or approximate).

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Section: Arm and Deeplearningmentioning

confidence: 99%

Association Rules Mining

Hamdad

Benatchba

2021

SN COMPUT. SCI.

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“…These algorithms can also learn from past transactions and continuously adapt to new fraud patterns. This helps prevent fraudulent transactions and protects both the customers and the businesses from financial losses [12].…”

Section: Introductionmentioning

confidence: 99%

Payments With Palm Using Ai and Ml Algorithms

2023

IRJMETS

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Payments has changed drastically over decades talking from barter system to using currencies. So over the past few years we are able to observe many changes in the digital payments and many modes to pay take for instance apple pay we can use either phone, card else smart gadgets like apple watches to make payments and now we are taking this change to the next level and introducing palm payments and this is the future of payments [1].The proposed system for palm payments is used to make payments using palm lines as every ones palm lines are different we will be using lidar for scanning line movements and using AI and ML algorithms to process accurate results [2]. So we will be having checkout page as we generally see in any of the websites and usually we select either debit else credit card and PayPal and some digital payment modes but in our case we will be seeing palm symbol which indicates palm payments which is secure and fastest means of payment [3]. The system also checks weather it is hand is original else it's a picture and also dead or alive or any suspicious activity going on. if anything goes wrong it also enables two factor authentication for security purpose it may include either OTP for mobile else security question which is safest way to make transaction. So in order to use this feature user has to enroll himself in palm payments which is one time enrollment and can be used for life time and has some age limitations (Pandey, 2021) Hence, the proposed system can exclusively reduce the time for payments it is slowly expanding in metro cities in united states and amazon has started trials in some of the big cities like new York slowly it is getting recognized and soon it may become effective and efficient way of payments.

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